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Jurnal Pertanian Tropik e-ISSN NO :2356- 4725/p-ISSN : 2655-7576

Vol.6. No.2, Agustus 2019 (29) 238- 249 https://jurnal.usu.ac.id/index.php/Tropik

238

Saepo Modi and Correlation among Statistical Methods for Measuring of Phenotypic

Stability

Saepo Modi dan Korelasi antarmetode Statistik untuk Mengukur Stabillitas Fenotipe

Sabam Malau1*, Albiner Siagian2, Maria Rumondang Sihotang3, Rosnawyta Simanjuntak4,

N.D.M. Romauli Hutabarat5

1 Agroecotechnology Department, Agriculture Faculty, Universitas HKBP Nommensen, Jalan

Sutomo 4-A, Medan 20234, Indonesia. 2 Public Health Department, Universitas Sumatera Utara, Jalan Abdul Hakim 1, Medan,

Indonesia. 3 Agribusiness Department, Agriculture Faculty, Universitas HKBP Nommensen, Jalan

Sutomo 4-A, Medan 20234, Indonesia. 4 Technology of Agriculture Product Department, Agriculture Faculty, Universitas HKBP

Nommensen, Jalan Sutomo 4-A, Medan 20234, Indonesia. 5 Food Science and Technology, Faculty of Agriculture, Universitas Sumatera Utara, Jalan

Jalan Prof A Sofyan 3, Medan, Indonesia.

*Corresponding author : [email protected]

ABSTRACT

A stabile phenotype is desired. Many statistical methods are available to measure stability.

So far, the choice of parameter of stability depended on the perception on the interaction of

genotype and environment or ease of counting. The goal of this research was to study

correlation among stability parameters. In total of 16 stability parameters were used in this

research. Saepo modi (SMsp) as a stability parameter was also used. Branch rust incidence,

leaf rust incidence, and leaf rust severity on Arabica coffee were used as variables. This

research result showed that none of the parameters of stability correlated significantly with all

parameter of stability. It coud be concluded that if someone want to use only one stability

parameter, it is preffered to make use of regression and deviation from regression (D2i). In

the case a researcher needs to use several of parameters of stability, Saepo modi (SMsp) might

be exercised.

Keywords: genotypes, interaction, leaf rust, parameter

ABSTRAK

Suatu fenotipe yang stabil yang diharapkan. Banyak metode statistik tersedia untuk mengukur

stabilitas. Sejauh ini, pemilihan parameter stabilitas tergantung kepada persepsi tentang

interaksi genotipe dengan lingkungan atau kemudahan perhitungan. Tujuan dari penelitian ini

adalah untuk mengkaji korelasi antarparameter stabilitas. Sebanyak 16 parameter stabilitas

digunakan pada penelitian ini. Saepo modi (SMsp) sebagai suatu parameter stabilitas juga

digunakan. Insiden karat cabang, insiden karat daun dan keparahan karat daun pada kopi

Arabica digunakan sebagai variabel. Hasil penelitian ini menunjukkan bahwa tidak satupun

dari parameter stabilitas berkorelasi signifikan dengan semua parameter stabilitas. Kesimpulan

penelitian ini adalah jika seseorang ingin menggunakan hanya satu parameter stabilitas, ia



239

disarankan menggunakan regresi dan deviasi dari regresi (D2i). Dalam hal seorang peneliti

ingin menggunakan beberapa parameter stabilitas, Saepo modi (SMsp) dapat digunakan.

Kata kunci: genotipe, interaksi, karat daun, parameter

INTRODUCTION

Task of plant breeding is to create

the best genotype with high quantity,

quality and stability of production over a

wide range of growth environment.

Experiments over seasons and years must

be carried out to test the stability of

genotypes perfing certain phenotypes

because rank of genotypes could be

changed in differen environment due to

interaction of genotype and enviroment.

Test of stability was used in the important

commodities such as barley (Sabaghnia et

al., 2012), black spure (Khalil, 1984),

Chenopodium spp (Bhargava et al., 2005),

chili pepper (Syukur etal., 2014), durum

wheat (Akcura et al., 2006), faba bean

(Temesgen et al., 2015), field pea (Fikere et

al., 2010), lentil (Dehghani et al., 2008),

maize (Scapim et al., 2000), oilseed rape

(Brandle and McVetty, 1988; Oghan et al.,

2013), potato (Flis et al., 2014), rice

(Purbokurniawan et al., 2014, Balakrishnan

et al., 2016), rubber tree (Silva et al., 2014),

sorghum (Adugna, 2008), sugar cane (Rea

et al., 2017), sweet potato (Bacusmo et al.,

1988), and vetch (Sayar et al., 2013).

The concepts of stability comprises

dynamic and static stability. Dynamic

stability of phenotype describes the ability

of a genotype to increase its performance in

better growth environment as well as to

decrease its performance in worse growth

condition. Static phenotypic stability

explains the ability of genotype to perform

constantly in various growth environment.

These concepts cause different statistical

methods which are variance analysis

(Roemer, 1917), regression (Finlay and

Wilkinson, 1963), and non-parametric (Lin

and Binns, 1988). Number of statistical

methods for measuring stability increased

(Becker and Leon, 1988).

Researchers use one or several of

them based on the assumptions about the

nature of G x E interaction as well as the

need for an easy statistical calculation. It is

needed to study correlation among those

stability parameters. The objectives of this

research was to study correlation among

parameter of statistical methods for

measuring of phenotypic stability. It was

hypothesized that there was significant

correlations among parameter of stabilities.

Result of this research was expected to

contribute to better understanding of

analysis of stability as well as to help

choosing appropriate parameter of stability.

MATERIAL AND METHOD

General forms of the observed

value of k genotypes in n environments is

presented in Table 2. Xij is the observed

value of the genotype ith at the environment

jth. X̅i.is the average performance of the

genotype ith at the environments j (j =

1,2,3,..... n). X̅.j is the mean value of

environment jth across the genotypes i (i =

1,2,3,..... k). X̅.. is the general mean of all

genotypes across the environments. X.. is

the grand total of the observed values.



240

Table 2. Hypothetical performance of k genotypes in n environments.

Genotype Environment Total Mean

Envi-1 Envi-2 Envi-3 Envi-j

Code 1 2 3 . . j n

G1 (1) X11 X12 X13 ... ... X1j X1. X̅1.

G2 (2) X21 X22 X23 ... ... X1j X2. X̅2.

G3 (3) X31 X32 X33 ... ... X1j X3. X̅3.

. ... ... ... ... ... ... ... ...

. ... ... ... ... ... ... ... ...

Gi (i) Xi1 Xi2 Xi3 ... ... Xij Xi. X̅i.

Total k X.1 X.2 X.3 ... ... X.j X..

Mean X̅.1 X̅.2 X̅.3 X̅.j X̅..

In this research, sixteen of the

statistical methods for measuring the

stability were used. They are based on the

variance analysis (Roemer, 1917; Plaisted

and Peterson, 1959; Plaisted, 1960;

Wricke, 1962; Shukla, 1972; Francis and

Kannenberg, 1978), regression (Finlay and

Wilkinson, 1963; Eberhart and Russel,

1966; Perkins and Jinks, 1968; Hanson,

1970; Tai, 1971; Pinthus, 1973), and non-

parametric (Lin and Binns, 1988; Kang,

1993).

Roemer (1917) proposed the

deviation of the performance of the

genotype from the genotypic mean (xij - x̅i.)

as the indication of the enviromental effect.

The environmental variance of the

genotype across the environments (si2) is

the measurement of stability of genotype.

The genotype that shows the smallest si2 is

the most stable. The greatest stability is si2

close to 0. The formula is

si2 = ∑

(xij− xi̅.)2

k−1

n

j=1.

Plaisted and Peterson (1959)

suggested the mean variance component for

pairwise GxE interaction (θi) as stability

measure. The genotype with smaller θi is

more stable. The formula is

θi =k

2(k−1)(n−1)+ ∑ [(xij −n

j=1

x̅i.) − (x̅.j − x̅..)]2 +

∑ ∑ [(xij− x̅i.)− (x̅.j−x̅..)]2n

j=1ki=1

2(k−1)(n−1).

Plaisted (1960) proposed the GxE

interaction variance from the subset (θ(i)) as

measurement of stability index of the

genotype. The smaller the θ(i) is, the more

stable the genotype is. The formula is

θ(i) =−k

(k−1)(k−2)/(n−1)+

∑ [(xij − x̅i.) − (x̅.j − x̅..)]2nj=1 +

∑ ∑ [(xij− x̅i.)− (x̅.j−x̅..)]2n

j=1ki=1

(k−2)(n−1).

Wricke (1962) offered the idea that

the genotypes contribute to the G x E

interaction. The deviation of the genotypic

effect (xij - x̅i.) from the environmental

effect (x̅.j - x̅..) is the genotypic contribution

to G x E interaction. The magnitude of this

deviation is measured by the variance (W2i)

termed ecovalence. The least W2i (the

highest ecovlaence) is the most stable. The

greatest stability is W2i = 0. The formula is

Wi2 = ∑ [(xij − x̅i.) − (x̅.j −n

j=1

x̅..)]2. Shukla (1972) proposed stability

variance (σ2i) which is the partitioning of

the G x E sum of square into component for

each genotype separately. The smaller σ2i

is, the more stable is. The most stable is

genotype with σ2i = 0. Negative value of σ2

i

is equal to zero. The formula is



241

σi2 =

k

(k−2)(n−1)+ ∑ [(xij −n

j=1

x̅i.) − (x̅.j − x̅..)]2 +

∑ ∑ [(xij− x̅i.)− (x̅.j−x̅..)]2n

j=1ki=1

(k−1)(k−2)(n−1).

Francis and Kannenberg (1978)

proposed relative variability as measument

of stability. Relative variability is

repesented by coefficient of variation (CV).

Genotype with small CV is stable. CV

close to 0 is the greatest stability. The

formula is

CVi =

√∑(xij− x̅i.)

2

k−1

n

j=1

x̅i. 𝑥 100%.

Finlay and Wilkinson (1963)

proposed regression coefficient as measure

of stability. The unity coefficient is |𝑏𝑖 = −1|. The genotype with b = 1 is the

most stable. The formula is

bi =∑ (xij− x̅i.)( x̅.j−x̅..)

nj=1

∑ (x.j− x̅..)2n

j=1

.

Eberhart and Russel (1966) defined

the measure of stability as residual mean

square of deviation from the regression

(δ2i). The genotype with smaller δ2

i is more

stable. The formula is

𝛿𝐼2 =

1

𝑛−2[∑ (xij − x̅i.)

2𝑛𝑗=1 −

𝛽𝑖2 ∑ (x.j − x̅..)

2nj=1 ].

Perkins and Jinks (1968) proposed

the regression coefficient (βi) and the

deviation from the regression line of each

environment (ψ2i) as the measure of

stability. A genotype is considered stable if

βi = 0 and ψ2i = 0. The formula are

𝛽𝑖 =∑ [(xij− x̅i.)− (x̅.j−x̅..)]n

j=1 (x.j− x̅..)

∑ (x.j− x̅..)2n

j=1

,

and

𝜓𝐼2 =

1

𝑛−2[∑ [(xij − x̅i.) −n

j=1

(x̅.j − x̅..)]2 − 𝛽𝑖2 ∑ (x.j − x̅..)

2nj=1 ].

Hanson (1970) proposed regression

and deviation from regression (D2i) as

stability measurement. The magnitude of

the deviation of the genotypic effect (xij -

x̅i.) from the environmental effect (x̅.j - x̅..)

is calculated by using the minimum

observed simple coefficient regression b

(bm). The stable gonotype does not deviate

from the straight line. The genotype with

smaller D2i is more stable. The formula is

𝐷𝑖2 = ∑ [(xij − x̅i.) − 𝑏𝑚(x̅.j +𝑛

𝑗=1

x̅..)]2. Tai (1971) proposed the partitioning

of GxE interaction into the linear response

of genotype to the environmental effect (αi)

and the deviation from the linear response

(λi) as measure of stability. The stability of

genotype is characterize by αi and λi. The

genotype with (αi = -1, λi = 1) is the most

stable, while the genotype with (αi = 0, λi =

1) has an average stability across

environment. The gonotype with (αi, λi) <

(0,1) performs an above-average stability,

and genotype with (αi, λi) > (0,1) shows a

below-average stability. The formula are

𝛼𝑖 =

{∑ ( x̅.j−x̅..)[(xij− x̅i.)−(x̅.j−x.̅.)]𝑛

𝑗=1 }/(𝑛−1)

(𝑀𝑆𝐸𝑛𝑣−𝑀𝑆𝑅𝑒𝑝(𝐸𝑛𝑣))/(𝑘𝑟), and

𝜆𝑖 =

{{∑ [(xij− x̅i.)−(x̅.j−x̅..)]2𝑛

𝑗=1 ]}

𝑛−1}−𝛼𝑖{

{∑ (x̅.j−x̅..)[𝑛𝑗=1 (xij− x̅i.)−(x̅.j−x̅..)]}

𝑛−1}

[(𝑘−1)𝑀𝑆𝐸𝑟𝑟𝑜𝑟]/(𝑘𝑟)

,

whereby MS mean square, MSEnv = MS of

environment (location), MSRep(Env) = MS of

replication within environment, k =

number of genotype, n = number of

environment, MSError = MS of error (MS of

pooled error), and r = number of

replication.

Pinthus (1973) proposed the

amount of the variation in genotypes that

can be explained by the variation of

environment i.e coefficient of

determination (r2) as stability measure. The

coefficient of determination is calculated as

the proportion of variation in genotypes

from the total variation. The value of r2

ranks is 0 (0%) to 1 (100%). The genotype

with r2 = 1 is the most stable. The formula

is

𝑟2 = {∑ (xij− x̅i.)( x̅.j−x̅..)

nj=1

∑ (x.j− x̅..)2n

j=1

}2.



242

Lin and Binns (1988) proposed the

superiority of genotype over a series of

environments (n) as measure of stability.

The superiority of the genotype is the

distance of the genotype performance from

the maximal performance for that

environment (xij - Maxj). The genotype

with smaller variance of the superiority of

the genotype averaged over all

environments (Pi) is more stable. The

formula is

Pi = ∑(xij−Maxj)2

2n

nj=1 .

Kang (1993) proposed the sum of

rank (𝜅i) of the observed performance of the genotype (Rig) with rank of of the genotype

based on Sukla’s stability variance (Ris) as

measure of stability. Based on the genotype

performance, the genotype with the highest

performance is number 1. Based on the

Sukla’s stability variance, the genotype

with the smallest Sukla’s stability get

number 1. The genotype with smaller 𝜅i is

more stable. The formula is

𝜅𝑖 = 𝑅𝑖𝑔 + 𝑅𝑖𝑠.

The authors of this paper propose

the frequency as the basis for analysis

which is termed saepe modi (Latin)

abbreviated as SM. The best parameter of

stability is the one that has the highest

number of significant correlations (SMsp)

with other parameters. This method might

be a nonparametric method.

Data of branch rust incidence, leaf

rust incidence, and leaf rust severity on

coffee leaves from coffee a field xperiment

were used.

RESULT AND DISCUSSION

Analysis of stability parameters

showed that none of the stability parameters

were significantly correlated with all

stability parameters in each phenotype

(Table 1). Stability parameter D2i of

Hanson (1970) for branch rust incidence

correlated most frequently (SMsp = 11) with

other parameters of stability. However,

stability parameter ψ2i of Perkins and Jinks

(1968) and CVi of Francis and Kannenberg (1978) for leaf rust incidence had the

highest frequency (SMsp = 11). Stability

parameter s2i of Roemer (1917), δ2

i of

Eberhart and Russel (1966), D2i of Hanson

(1970), and r2i of Pinthus (1973) for leaf

rust severity performed the same highest

frequency (SMsp = 13). The research result

also showed that number of the frequency

of the significant correlation among

stability parameters were different if

phenotypes were different (Table 1). The

numbers ranged from 2 to11, 4 to 11and 1

to13 for branch rust incidence, leaf rust

incidence and leaf rust severity,

respectively. Out of the total number of

correlation over three phenotypes of each

stability parameter (45), D2i of Hanson

(1970) had the highest number of the

significant correlations (SMsp = 33).

These research results implicated

that none of the stability parameters could

represent all stability parameters. Based on

that, several stability parameters should be

used to obtain a strong basis for choosing

the most stable phenotype or the most

stable genotypes in a certain phenotype. In

the case of selecting one parameter of

stability, however, D2i of Hanson (1970)

could be considered to be chosen. This

suggestion was in contrary with research

result of Temesgen et al. (2015). Choosing

D2i of Hanson (1970) as parameter of

stability based on the saepe modi (Table 1,

SMsp = 33) was entirely different with other

selecting methods based which wa based on

the prediction of the nature of G x E

interaction and the need of a simple

statistical method (Freeman, 1973; Becker,

1981; Lin et al., 1986; St.Clair and

Kleinschmit, 1986; Becker and Leon, 1988;

Bacusmo et al., 1988; Magari and Kang,

1993; Piepho, 1994; Ferreira et al., 2006;

Souza et al., 2007; Mitrovic et al., 2011;

Nascimento et al., 2013; Syukur etal., 2014;

Balakrishnan et al., 2016).



243

Table 1. Correlation among parameters of stability in branch rust incidence, leaf rust incidence, and leaf rust severity

Variance Regression Nonparametric

Author and parameter Author and parameter Author and parameter

Parameter

Variable

Roemer

(1917)

Plaisted and Peters

on (1959)

Plaisted

(1960) Wricke (1962)

Shukla (1972)

Francis and

Kannenberg (1978)

Finlay and

Wilkinson

(1963)

Eberhart and Russel (1966)

Perkins and Jinks

(1968)

Perkins and Jinks

(1968)

Hanson

(1970) Tai

(1971) Tai

(1971 Pinthus (1973)

Linn and

Binns (1978)

Kang (1993)

s2i θi θ(i) W2i σ2i cvi bi δ2i βi ψ2i Di2 αi λi ri2 Pi 𝜅i SMsp si

2

BRI

1 0.541 ns

-0.541 ns

0.541 ns

0.541 ns

0.593 ns

0.988 **

0.999 **

0.987 **

0.259 ns

0.765 *

0.988 **

0.408 ns

-0.261 ns

0.156 ns

0.484 ns

5

LRI

1 0.484 ns

-0.484 ns

0.484 ns

0.457 ns

0.767 *

0.942 **

1.000 **

0.941 **

0.999 **

0.703 ns

0.940 **

0.465 ns

-0.415 ns

0.175 ns

0.300 ns

6

LRS

1 0.886 **

-0.887 **

0.886 **

0.886 **

0.987 **

0.902 **

0.997 **

0.902 **

0.905 **

0.979 **

0.902 **

0.903 **

0.979 **

0.097 ns

0.610 ns

13

θi

BRI

1 -1.000 **

1.000 **

1.000 **

0.490 ns

0.402 ns

0.546 ns

0.400 ns

0.870 *

0.956 **

0.403 ns

0.965 **

-0.925 **

0.391 Ns

0.782 *

8

LRI

1 -1.000 **

1.000 **

1.000 **

0.848 *

0.162 ns

0.495 ns

0.160 ns

0.993 **

0.963 **

0.158 ns

0.997 **

-0.993 *

0.887 **

0.811 *

10

LRS

1 -1.000 **

1.000 **

1.000 **

0.898 **

0.600 ns

0.866 *

0.600 ns

0.965 **

0.963 **

0.600 ns

-0.992 **

0.963 **

0.377 ns

0.702 ns

10

θ(i)

BRI

1 -1.000 **

-1.000 **

-0.490 ns

-0.402 ns

-0.546 ns

0.400 ns

-0.870 *

-0.956 **

-0.403 ns

-0.965 **

0.925 **

-0.391 ns

-0.782 *

8

LRI

1 -1.000 **

-1.000 **

-0.848 *

-0.162 ns

.-0.495 ns

-0.160 ns

-0.993 **

-0.963 **

-0.158 ns

-0.997 **

0.993 *

-0.887 **

-0.811 *

10

LRS

1 -1.000 **

-1.000 **

-0.898 **

-0.600 ns

-0.866 *

-0.600 ns

-0.965 **

-0.963 **

-0.600 ns

0.992 **

-0.963 **

-0.377 ns

-0.701 ns

10

W2i

BRI

1 1.000 **

0.490 ns

0.402 ns

0.546 ns

0.400 ns

0.870 *

0.956 **

0.403 ns

0.965 **

-0.925 **

0.391 ns

0.782 *

8

LRI

1 1.000 **

0.848 *

0.162 ns

0.495 ns

0.160 ns

0.993 **

0.963 **

0.158 ns

0.997 **

-0.993 *

0.887 **

0.811 *

10

LRS

1 1.000 **

0.898 **

0.600 ns

0.866 *

0.600 ns

0.965 **

0.963 **

0.600 ns

0.992 **

0.963 **

0.377 ns

0.702 ns

10

σ2i

BRI

1 0.490 ns

0.402 ns

0.546 ns

0.400 ns

0.870 *

0.956 **

0.403 ns

0.965 **

-0.925 **

0.391 ns

0.782 *

8

LRI

1 0.848 *

0.162 ns

0.495 ns

0.160 ns

0.993 **

0.963 **

0.158 ns

0.997 **

-0.993 **

0.887 **

0.811 *

10

LRS

1 0.898 **

0.600 ns

0.866 *

0.600 ns

0.965 **

0.963 **

0.600 ns

0.992 **

0.963 **

0.377 ns

0.702 ns

10



244

cvi

BRI

1 0.553 ns

0.620 ns

0.553 ns

0.609 ns

0.583 ns

0.554 ns

0.570 ns

0.570 ns

0.878 **

0.861 *

2

LRI

1 0.540 ns

0.772 *

0.538 ns

0.825 *

0.927 **

0.536 ns

0.831 *

-0.805 *

0.750 ns

0.766 *

11

LRS

1 0.869 **

0.981 **

0.869 *

0.903 **

0.976 **

0.869 *

0.908 **

0.976 **

0.220 ns

0.716 ns

13

bi

BRI

1 0.986 **

1.000 **

0.118 **

0.654 **

1.000 **

0.264 ns

-0.111 ns

0.096 ns

0.380 ns

6

LRI

1 0.937 **

1.000 **

0.134 ns

0.423 ns

1.000 **

0.141 ns

-0.086 ns

-0.143 ns

0.028 ns

4

LRS

1 0.917 **

1.000 **

0.665 ns

0.794 *

1.000 **

0.637 ns

0.794 *

-0.184 ns

0.400 ns

7

δ2i

BRI

1 0.985 **

0.281 ns

0.769 *

0.986 **

0.423 ns

-0.276 ns

0.167 ns

0.504 ns

5

LRI

1 0.936 **

0.471 ns

0.712 ns

0.936 **

0.478 ns

-0.426 ns

0.184 ns

0.301 ns

5

LRS

1 0.917 **

0.903 **

0.968 **

0.917 **

0.892 **

0.952 **

0.062 ns

0.593 ns

13

βi

BRI

1 0.117 ns

0.653 ns

1.000 **

0.263 ns

-0.109 ns

0.096 ns

0.379 ns

4

LRI

1 0.132 ns

0.421 ns

1.000 **

0.139 ns

-0.084 ns

-0.145 ns

0.026 ns

4

LRS

1 0.665 ns

0.794 *

1.000 **

0.637 ns

0.794 *

-0.184 ns

0.400 ns

7

ψ2i

BRI

1 0.757 *

0.120 ns

0.969 **

-0.973 **

0.665 ns

0.841 *

9

LRI

1 0.949 **

0.130 ns

0.999 **

-0.998 **

0.883 **

0.765 *

11

LRS

1 0.958 **

0.665 ns

0.991 **

0.958 **

0.267 ns

0.669 ns

10

Di2

BRI

1 0.655 ns

0.882 **

-0.800 *

0.354 ns

0.769 *

11

LRI

1 0.419 ns

0.955 **

-0.936 **

0.775 *

0.752 ns

9

LRS

1 0.794 *

0.969 **

1.000 **

0.224 ns

0.668 ns

13

αi

BRI

1 0.265 ns

-0.112 ns

0.098 ns

0.381 ns

4

LRI

1 0.137 ns

-0.082 ns

-0.147 ns

0.024 ns

4

LRS 1 0.637 0.794 -184 0.400 7



245

ns * ns ns

λi

BRI

1 -0.982 **

0.550 ns

0.840 *

8

LRI

1 -0.998 **

0.883 **

0.786 *

10

LRS

1 0.969 **

0.327 ns

0.692 ns

10

ri2

BRI

1 0.550 ns

0.840 *

8

LRI

1 -0.895 **

-0.789 *

10

LRS

1 0.224 ns

0.668 ns

13

Pi

BRI

1 0.843 *

2

LRI

1 0.841 *

9

LRS

1 0.801 *

1

𝜅i BRI 1 10

LRI 1 9 LRS 1 1 SMsp Total 24 28 28 28 28 26 17 23 15 30 33 15 28 31 12 20

n = 28, BRI = branch rust incidence, LRI = leaf rust incidence, LRS = leaf rust severity, ns = not significant, * = significant at α 0.05

= 0.374, and ** = highly significant at α 0.01 = 0.478, SMsp = number of frequency of parameter correlating significantly with others.



246

CONCLUSION

This result proved that none of the

parameters of stability could represent all

stability all parameters. If someone prefer

to use one parameter of stability, it is

suggested to use regression and deviation

from regression (D2i) proposed by Hanson

(1970). However, if a researcher needs to

use many stability parameters, Saepo modi

(SMsp) could be considered to be used.

ACKNOWLEDGEMENT

The authors thank the Ministry of

Research, Technology and Higher

Education for funding this research with

grant number 0100/E5.1/PE/2015 dated

January 19, 2015.

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